Many more improvements are seen when our post hoc validation and correction methodology, described in Section 5, is used, but the merging of fragments derived from different on-line systems also leads to an improvement in translation quality.

consider the examples in (45): (45) Input: an old story in common System B: une vieille histoire dans commun System combination Bc: une vieille histoire en commun the optional PP in common was mistranslated by system B as dans commun, but when knowledge from system c is added to that of system B, the improved translation en commun is generated.


This is an interesting result given that in Table 5, only 57.7% of the NPs translated by system B were ranked first, with 60% of those produced by system C ranked in first place.

we see a 6.4% improvement (31 NPs) when fragments from systems B and C are combined.


Where K=1, we view coverage and translation quality as equally important.

if we consider quality to be more important, we can increase K. We provide results for K=1 and K=2 in 48 where K=1, wEBMT outperforms SDL by a factor of 22, whereas there is no gain with respect to Reverso, and a slight loss compared to Logomedia.


With the counts for all other bigrams for the two translation candidates in (51) being exactly the same, in order to evaluate which of these proposed target strings is the "better" translation, one can simply add the occurrences found on the Web for all four different bigrams and report their relative probabilities.

this gives us the probabilities in (52): the string empire est au is about 2.4 times more likely than the string empire sont au.


However, the major improvement that can be expected is in the segmentation process: Given that verbs are not a closed class, any verb will be contained within (part of) a chunk pertaining to its subject NP.

although subject-verb agreement poses a considerable problem to our system given the choice of original input material, this particular instance of boundary friction will disappear if we segment our translation pairs at the sentential rather than at the phrasal level.


Our approach estimates Web frequencies based not on bigram counts directly, but on page counts.

it ignores the fact that a bigram can occur more than once on a given Web page.


The results in Tables 13 and 14 show the pseudodisambiguation performance achieved for the best parameter settings.

these results were obtained on the development set (i.e., on the same data set that was used to optimize the parameters), not on a completely unseen test set.


This comparison is based on the hypothesis that the terms in an appropriate directory for a word sense will have some correlation with the sense description via WordNet semantic relations.

our assumption is that the path to the directory in the ODP topical structure will have some degree of overlapping with the hyponymy path to the word sense in the WordNet hierarchical structure.


Modifiers are detected with a set of simple patterns, as the syntactic properties of descriptions in directories are quite simple.

we discard most cases using the structure of the ODP hierarchy, as in this case.


In spite of being manually supervised, Web directories offer immense structured corpora that deserve our attention as sources of linguistic information.

listing word sense/ODP directory associations has the additional advantage, compared to other Web-mining approaches, of providing a wealth of lexical information in a very condensed manner.


For the purposes of this article we can summarize aspects of Turkish as follows: • The IGs are the "words."

we treat a chunk of (free and bound) inflectional morphemes as the units that we relate with dependency links.


Let T 1 be a transducer that maps between regular languages U 1 and L 1 , and let T 2 be a transducer that maps between regular languages U 2 and L 2 .

the composition t of t 1 and t 2 , denoted by t 1 • t 2 , is the transducer that maps between the resulting mapping is defined only for the respective images, in t −1 Note that the composition operation is order dependent; t 1 • t 2 is not the same mapping as t 2 • t 1 .


• The channel symbol 1 indicates that the channel is used by a link that starts at some IG on the left and ends at some IG on the right.

the link is just "crossing over" this IG.


Finally, discourse adverbials, like other anaphors, may require semantic representations in which their arguments are bound variables ranging over discourse entities.

whereas it might be possible to represent Although P, Q using a binary modal operator (21) although (p, q) where formulas p and q translate the sentences P and Q that although combines, we cannot represent P .


The extension we make to the general framework presented above in order to include discourse adverbials as discourse anaphors is to allow more general functions f α to be associated with lexically specified anaphors.

for the discourse adverbials considered in this article, the function associated with an adverbial maps its anaphoric argument-an eventuality derived from the current discourse context-to a function that applies to the interpretation of the adverbial's matrix clause (itself an eventuality).


We treat this coercion as a type of associative or bridging inference, as in the examples discussed in section 3.1.

e 2 = culmination(e 1 )∈assoc(e 1 ), where e 1 :go-west(you, lancaster ave) Taking this e 2 as the anaphoric argument EV of then yields the proposition the eventuality of turning right onto County Line is after that of going west on Lancaster Avenue to County Line.


if (VE, x), where complement (VE, EV) where variable EV is resolved anaphorically to an eventuality in the current discourse context that admits a complement.

otherwise requires a contextually relevant complement to its antecedent and asserts that if that complement holds, the argument to the λ-expression will as well.


At the level of LF, the abstract object e 6 that is associated with the conditional relation serves as one argument to the explanation relation contributed by because, with e 2 being the other.

because and otherwise together end up contributing explanation(e 2 ,e 6 ) (i.e., your needing to stop is explained by the fact that if you do something other than stop, you'll get a ticket).


The interpretation of for example here involves abstracting the meaning of its matrix structure with respect to the material to its right, then making an assertion with respect to this abstraction.

if the LF contributed by the matrix clause of (42A) is, roughly, i. contain(box1,hematite1) then the LF resulting from the addition of for example can be written either with set notation (as in (ii)), taking an entity to exemplify a set, or with λ-notation (as in (iii)), taking an entity to exemplify a property: ii.


The other explanation follows the same pattern as the examples given above, but with no further relation than elaboration (σ,δ).

we understand in (48) that having database crossreferencing facilities elaborates the respects in which hypertext books are superior to normal books, whereas in (49), we understand that the Herald's [newspaper] consistently beating its much-larger rival elaborates the claim that its accomplishments were notable.


one cannot paraphrase (51) as (52) as in (43) where for example follows so: (52) All the children are ill, so for example Andrew can't help out in the shop.


For example, the verb likes anchors one tree corresponding to John likes apples, another corresponding to the topicalized Apples John likes, a third corresponding to the passive Apples are liked by John, and others as well.

there is a tree for each minimal syntactic construction in which likes can appear, all sharing the same predicate-argument structure.


Both trees in the tree set participate in the derivation: The auxiliary tree β:for ex1 adjoins at the root of T2, whereas the auxiliary tree β:for ex2 adjoins at the root of the higher discourse unit.

since we saw from example (66a) that the interpretation of this higher discourse unit is explanation(interp(T2),interp(T1)), the interpretation associated with the adjoined β:for ex2 node both embeds and abstracts this interpretation, yielding John's never returning what he borrows is one instance of a set of explanations.


This treatment has the added advantage that one does not have to treat This is not because as a separate cue phrase.

rather, negation simply produces i1: ¬explanation(interp(TB),interp(T1)) i2: elaboration(i1,interp(T1)) T1 is elaborated by a denial of a (possible) explanation.


There are two critical questions regarding the parameterization of a parsing approach: Which linguistic objects (e.g., context-free rules, parse moves) should the model's parameters be associated with?

which features should be used to discriminate among alternative parse trees?


One hope is that an integrated treatment of traces will improve the parameterization of the model.

the subcategorization probabilities are smeared by extraction.


Base-NPs deserve special treatment for three reasons: • The boundaries of base-NPs are often strongly marked.

the start points of base-NPs are often marked with a determiner or another distinctive item, such as an adjective.


• The independence assumptions are different when the parent nonterminal is an NPB.

equations (5) and (6) are modified to be The modifier and previous-modifier nonterminals are always adjacent, so the distance variable is constant and is omitted.


Previous work-the generative models described in Collins (1996) and the earlier version of these models described in Collins (1997)-conditioned on punctuation as surface features of the string, treating it quite differently from lexical items.

the model in Collins (1997) failed to generate punctuation, a deficiency of the model.


Looking at the parser's output, the reason for this poor performance is that the adjacency condition in the distance measure is approximating subcategorization information.

in phrases such as PPs and SBARs (and, to a lesser extent, in VPs) that almost always take exactly one complement to the right of their head, the adjacency feature encodes this monovalency through parameters P(STOP|PP/SBAR, adjacent) = 0 and P(STOP|PP/SBAR, not adjacent) = 1.


These rules account for 0.72% of rules by token.

if one of the 939,382 rule tokens in sections 2-21 of the treebank were drawn at random, there would be a 0.72% chance of its being the only instance of that rule in the 939,382 tokens.


This may be an important point to bear in mind when applying the models to other treebanks or other languages.

it may be important to perform transformations on some structures in treebanks with different annotation styles.


If, for example, we were to attempt to translate the English noun plant, which could refer either to a type of factory or to a form of flora (i.e., in Chinese, either to [gongchang] or to [zhiwu]), our goal would be to determine the correct Chinese translation.

word translation disambiguation is essentially a special case of word sense disambiguation (in the above example, gongchang would correspond to the sense of factory and zhiwu to the sense of flora).


we formalize the problem of word translation disambiguation as that of classification based on statistical learning.


We next use sentences such as G1 to estimate P (C) (e ε | t ε ) as described above.

with the frequency data f (c, t ε ) and EM we can estimate P (C) (e ε | t ε ).


The second implementation is different from the first one only in (1).

it employs a decision list as the classifier.


With one sense per discourse 54.7 69.3 75.5 Without one sense per discourse 54.6 66.4 71.6 We have addressed here the problem of classification across two languages.

we have considered the problem of bootstrapping.


In Experiment 1 we use the model to test the hypothesis that subcategorization information can be used to disambiguate polysemous verbs.

we concentrate on verbs like serve (see example (5)) that can be disambiguated solely on the basis of their frame.


The task was the following: Given that we know the frame of a given verb, can we predict its semantic class?

by varying the class c in the term P(c, f , v), we are trying to see whether the class that maximizes it is the one predicted by the lexical semantics and Table 8 Model accuracy using equal distribution of verb frequencies for the estimation of P(c).


Similarly, the right accessor variety R av (s) is defined as the number of distinct characters (successors) except "E" that succeed s plus the number of distinct sentences in which s appears at the end.

characters "S" and "E" are repeatedly counted.


For example, , 'of I', is in the h+core style and , 'I', is the core, , 'my', is in the core+t style and , 'I', is the core; and , 'of procedure is', is in the h+core+t style and , 'procedure', is the core.

none of the strings matching these three cases should be considered words, that is, they should be discarded.


In our experiments, we performed only one step of each of the ADHESIVE JUDGE rules (in either direction) for discarding meaningless multicharacter strings.

in any of the three styles (h+core, core+t, or h+core+t), only the leftmost or rightmost character was considered among all of the head-or tail-adhesive characters.


The same judgment process was used in the core+t and h+core+t styles.

only the first or last character, or both, of a string were used in rule (2) of the ADHESIVE JUDGE rules.


Beyond precision, another concern is partial recall.

how many words will be missed using such a method?


Second, κ is affected by skewed distributions of categories (the prevalence problem) and by the degree to which the coders disagree (the bias problem).

for a fixed P(A), the values of κ vary substantially in the presence of prevalence, bias, or both.


There are also many crossreferences between chapters, which allow the authors to build upon the foundation of one another's work and eliminate redundancies.

the contents consist of 38 survey papers grouped into three parts: Fundamentals; Processes, Methods, and Resources; and Applications.


Lafferty and Zhai claim to show an equivalence between the underlying probabilistic semantics of the language-modeling approach and the standard probabilistic model of information retrieval.


Lexical cohesion contributes to textual coherence.

strong semantic and structural relationships among words in a text help to make that piece of text "make sense."


First, in the 66 clauses with full coverage by the Gainen Base, we examined the 18 instances of what we had, on the basis of intuitive human judgment, designated COHESIVE transitions.

we focused on the noun(s) we had singled out in our revised hand-tabulation of the corpus (Table 5) as providing the lexical cohesion in these transitions.


The results in this table suggest that similarity scores can accurately represent relative coherence as characterized by the transitions in this small sample.

the similarity scores we have examined here reflect the relative load on processing imposed by each type of transition with between 86% and 100% accuracy, with groupwise scores being slightly more accurate than those based on the highest individual score.


In addition, similarity measures never predict a CONTINUE transition with a higher processing load (i.e., lower similarity score) than a COMPLETE SHIFT.

the relative positions of CONTINUE and COMPLETE SHIFT are always correct.


In addition, examination of the particular entities contributing to high levels of semantic similarity might also allow us to characterize the notion of "global topic," albeit in a differentiated way.

if we determine not just one semantic distance measure for the sets of entities in two adjacent utterances, but the individual distances for each combination of those entities (as briefly described in note 13), we can determine those entities that are contributing the greatest amount of semantic similarity to the measure and identify a cluster of entities that can be taken to represent a global topic.


In statistical machine translation, correspondences between the words in the source and the target language are learned from parallel corpora, and often little or no linguistic knowledge is used to structure the underlying models.

existing statistical systems for machine translation often treat different inflected forms of the same lemma as if they were independent of one another.


Some of these techniques are based on results from formal language theory.

complex regular grammars can be built by inferring simple grammars that recognize local languages (García, Vidal, and Casacuberta 1987).


We are interested only in transducers without useless states, that is, those in which for every state in T , there is a path leading to a final state.

if we further assume that P T P (s, t) is zero when no translation form exists for (s, t) in T , it can be easily verified that P T P is a joint distribution on Σ × ∆ which will be called the stochastic translation defined by T P .


As in Carrasco and Forcada (2002), we define δ : Q × Σ → Q as a total mapping.

if the automaton is not complete, that is, if ∃q ∈ Q ∧ ∃a ∈ Σ : δ(q, a) ∈ Q, then an absorption state ⊥ ∈ F such that ∀a ∈ Σ : δ(⊥, a) = ⊥ must be added to Q.


If you are already working with some finite-state toolbox (such as the FSM tools from AT&T [Mohri, Pereira, and Riley 1998] or van Noord and Gerdemann's [2001b] FSA utils), then this book will provide insight into the vast possibilities that the technology offers and will help you place your own work in context.

if you already work with LEXC or XFST, this is the bible of the applications.


Part III finishes with Lars Hoogweg extending DOP parsing with tree insertion, which broadens the kinds of substructures available in the DOP model.

modeling insertion provides access to simplifications of existing subtrees which result from the removal of modifiers and also allows existing structures to be combined in a richer set of ways.


Stevenson is concerned with the problem of how to discriminate multiple meanings or senses of the same word.

the book addresses the task of sense tagging, which Stevenson defines as the task of annotating all the words in a document with senses from a given lexicon.


Performance of the hypothesis: Testing the candidates on the current working hypothesis shows the type of input data on which the hypothesis may perform weakly.

if the current hypothesis is unable to label a candidate or is uncertain about it, then the candidate might be a good training example (Lewis and Catlett 1994).


Under f backoff , if u 1 were chosen as the first example, u 2 would lose all its utility, because we could not prune any extra characteristic tuples by using u 2 .

in the next round of selection, f backoff (u 2 , C) = 0.


4 Applying the confidence interval concept to evaluating candidates for the back-off PP-attachment model, we define a function f conf that scores a candidate by taking the average of the lengths of the confidence interval of each back-off level.

is the probability that model C will attach u to noun at back-off level l, and n l (u, C) is the number of training examples upon which this classification is based.


First, we define a function, called area(p, n), that computes the area under a Gaussian function N(x, µ, σ) with a mean of 0.5 and a standard deviation of 0.1 that is bounded by the confidence interval as computed by conf int(p, n) (see Figure 5).

5 supposep has a confidence interval of [a, b]; then Computing area for each back-off level, we define an evaluation function, f area (u, C), as their average.


To quantitatively characterize its distribution, we compute the entropy of the distribution.

where V is a random variable that can take any possible outcome in set V, and ) is the density function.


To normalize for the number of parses, the uncertainty-based evaluation function, f unc , is defined as a measurement of similarity between the actual probability distribution of the parses and a hypothetical uniform distribution for that set of parses.

we divide the tree entropy by the log of the number of parses: 10 We now derive the expression for TE (w, G).


We later asked the same annotators to identify the subjective elements in WSJ-SE.

each annotator was given the subjective sentences he identified in the previous study and asked to put brackets around the words he believed caused the sentence to be classified as subjective.


The proportion of clues in subjective documents or expressions is their precision.

the precision of a set S with respect to opinion pieces is prec(S) = number of instances of members of S in opinion pieces total number of instances of members of S in the data The precision of a set S with respect to subjective elements is prec(S) = number of instances of members of S in subjective elements total number of instances of members of S in the data In the above, S is a set of types (not tokens).


Baseline precision for an annotator is the proportion of words included in subjective elements by that annotator.

consider annotator M. The baseline precision of words in subjective elements marked by M is 0.08, Table 3 Frequencies and increases in precision for words that appear exactly once in the data sets composing OP1.


If be a scumbag does not have higher precision than scumbag alone, we do not want to select it.

let (W1, W2) be a bigram consisting of consecutive words W1 and W2.


In essence, we treat the set of single-instance words as a single, frequently occurring word (which occurs with various parts of speech).

the same method used for extracting and selecting n-grams above is used to obtain the potentially subjective collocations with one or more positions filled by a UNIQUE, part-of-speech pair.


The union of the retained sets will be denoted R T,n , that is, the union of all sets {s i } ∪ C i,n with precision on the training set > T. In Wiebe (2000), the seeds (the s i s) were extracted from the subjective-element annotations in corpus WSJ-SE.

the seeds were the adjectives that appear at least once in a subjective element in WSJ-SE.


We explore this idea in the current work, assessing whether PSEs are more likely to be subjective if they are surrounded by subjective elements.

we experiment with a density feature to decide whether or not a PSE instance is subjective: If a sufficient number of subjective elements are nearby, then the PSE instance is considered to be subjective; otherwise, it is discarded.


The researchers working on centering argue that while these concepts play a central role in any theory of discourse coherence and salience, their precise characterization is best left for subsequent research, indeed, that some of these concepts (e.g., ranking) might be defined in a different way for each language (Walker, Iida, and Cote 1994).

these notions should be viewed as parameters of centering.


Nevertheless, most versions of the theory developed since Kameyama (1985Kameyama ( , 1986 and Grosz, Joshi, and Weinstein (1986) have assumed that grammatical function plays the main role in determining the order among forward-looking centers, at least for English.

grosz, Joshi, and Weinstein (1995) claim that subjects are ranked more highly than objects, which are ranked more highly than other grammatical positions: SUBJ ≺ OBJ ≺ OTHERS (see also Kameyama 1986;Hudson, Tanenhaus, and Dell 1986).


Strube and Hahn (1999) argue that in German, the rank of discourse entities is determined by the position they hold in Prince's (1981Prince's ( , 1992 givenness hierarchy.

strube and Hahn argue that hearer-old entities rank higher than mediated entities, which and in turn rank higher than hearer-new entities: HEARER-OLD ≺ MEDIATED ≺ HEARER-NEW.


1997) as well as work by Stevenson, Crawley, and Kleinman (1994), and Pearson, Stevenson, and Poesio (2001).

there is evidence that with certain verbs, the normal preference for subjects to rank higher than their objects is reversed, although these preferences are modified by other factors such as order of mention, type of connective, and animacy (Stevenson, Crawley, and Kleinman 1994;Stevenson et al.


Given the many ways in which the parameters of centering can be set, the only feasible way to make a systematic comparison between the theory's "instantiations" is by computational means.

running computer simulations of the process of local focus update using an annotated corpus and comparing the results obtained under different instantiations.


The chance of error doesn't go below 10% even if we count the 163 utterances that do not contain references to CFs introduced in the previous utterance but are segment boundaries and therefore are not governed by the constraint.

if the vanilla instantiation were the "right" way of setting the parameters, we would have to conclude that in the genres contained in our corpus, utterances are very likely to have a unique CB, but entity coherence does not play a major role in ensuring that a text is coherent: only 35.4% of utterances in our corpus would be "entity-coherent," that is, would contain an explicit mention to an entity realized in the previous finite clause.


In general, the best results for C1 are obtained by considering larger chunks of text as a single utterance, thus reducing the number of utterances.

fewer violations are obtained by not considering as utterances finite clauses that occur as parentheticals, as subjects (as in That John could do this to Mary was a big surprise to me), and as matrix clauses with an empty subject (as in It is likely that John will arrive tomorrow).


Strube and Hahn's own version of Rule 2 still isn't verified, but this version of the rule is not verified by any of the instantiations we evaluated.

with the IS or IF instantiation and STRUBE-HAHN ranking, all three claims of the theory are verified at the .01 level.


A more sensible approach, especially as we don't yet know all the factors affecting coherence, would be to be more explicit about the scope of centering theory, viewing it not as a comprehensive account of "local coherence," but only as an account of the contribution of entity coherence to local coherence.

we could view (Entity) Continuity as only one among the preferences holding at the discourse level.


However, the RNP is observed only in a subset of the cases that would be considered as CB mentions according to the definition provided by Constraint 3, and in the example we are discussing (10), neither Branicki nor the cupboard occur in (u229) in a position that would be subject to RNP effects according to Gordon et al.


The "inner loop" or base learner is a supervised learning algorithm.

yarowsky uses a simple decision list learner that considers rules of the form "If instance x contains feature f , then predict label j" and selects those rules whose precision on the training data is highest.


It is assumed that the classifier makes confidence-weighted predictions.

the classifier defines a scoring function π(x, j), and the predicted label for example Ties are broken arbitrarily.


To complete an iteration of the Yarowsky algorithm, one recomputes labels for examples.

the labelŷ is assigned to example x if the score π x (ŷ) exceeds a threshold ζ, called the labeling threshold.


The likelihood of θ is the probability of the full data set according to the model, viewed as a function of θ, and the maximum-likelihood criterion instructs us to choose the parameter settingsθ that maximize likelihood, or equivalently, log-likelihood: represents the truth value of the proposition Φ; it is one if Φ is true and zero otherwise.)

let us define Note that φ x satisfies the formal requirements of a probability distribution over labels j: it is a point distribution with all its mass concentrated on Y x .


We assume that the base learner reduces divergence, if possible.

we assume with equality only if there is no classifier π (t+1) ∈ Π that makes ∆D Λ < 0.


We can guarantee that ) viewed as a function of p. By definition: We wish to find the distribution p that minimizes H(p||π ).

clearly, we accomplish that by placing all the mass of If there is more than one minimizer, H(p||π ) is minimized by any distribution p that distributes all its mass among the minimizers of − log π we minimize H(p||π , which is to say, by labeling x as predicted by π (t+1) .


Hence the value of H(x) at the end of the iteration, after the labeling step, is H 2 : As we observed above, H 1 > H 0 , but if we consider the change overall, we find that the increase in the training step is more than offset in the labeling step: When one speaks of the Yarowsky algorithm, one often has in mind not just the generic algorithm Y-0 (or Y-1), but an algorithm whose specification includes the particular choice of base learning algorithm made by Yarowsky.

yarowsky's base learner constructs a decision list, that is, a list of rules of form f → j, where f is a feature and j is a label, with score θ fj .


For this reason, we do not prune the list.

we represent a decision list as a set of parameters {θ fj }, one for every possible rule f → j in the cross product of the set of features and the set of labels.


Unfortunately, we cannot prove anything about DL-0 as it stands.

we are unable to show that DL-0 reduces divergence between prediction and labeling distributions (7).


DL-1-VS (Table 9) uses smoothed precision, but unlike DL-0, DL-1-VS does not use a fixed smoothing constant ; rather varies from feature to feature.

in computing the score θ fj , DL-1-VS uses |V f |/L as its value for .


DL-1 minimizes K as a function of θ, holding φ constant.

dL-1-R minimizes K over labeled examples Λ, and dL-1-VS minimizes K over all examples X.


An interesting range of update rules work in the sequential setting.

smoothed precision with fixed , as in the original algorithm Y-0/DL-0, works in the sequential setting, though with a proviso that will be spelled out later.


The label of example x is set toŷ if any feature of x belongs to S t+1 .

all previously labeled examples continue to be labeled (though their labels may change), and any unlabeled examples possessing feature f t become labeled.


Intuitively, we wish to reduce the uncertainty of the model's predictions, while also improving the fit between the model's predictions and the known labels.

let us focus now on the uncertainty of the model's predictions:  by decreasing the uncertainty of the prediction distributions of individual features and simultaneously increasing the agreement among features (that is, decreasing their pairwise divergence), we decrease an upper bound on H(π x ).


By the reasoning of the previous paragraph, we know that j † = j * at the minimum of (37).

hence we can minimize (37) by minimizing We compute the gradient: As before, the derivative of the constraint C f = 0 is one, and we minimize (38) under the constraint by solving Substituting into the constraint gives us Substituting this back into (40) yields: the maximizing solution is peaked precision, which is the update rule for YS-P. We now show that YS-R also decreases K in each iteration.


"Incremental" sentence-by-sentence generation, in which the syntactic structure of U n is determined before the semantic content of U n+1 is planned.

the text planner would plan the content of U n+1 by aiming to realize a proposition in the knowledge base which mentions an entity which is salient in U n .


We suggest the crucial criterion for such compatibility should be as follows: Any grouping of the elementary propositions in the TS must also occur in the RS.

the text structurer is allowed to eliminate groupings, but not to add any.


Together with Collins' thesis, this article contains all the information necessary to replicate Collins' parsing results.

2 this article describes all the as-yet-unpublished details and features of Collins' model and some analysis of the effect of these features with respect to parsing performance, as well as some comparative analysis of the effects of published features.


2 Specifically, this article describes all the as-yet-unpublished details and features of Collins' model and some analysis of the effect of these features with respect to parsing performance, as well as some comparative analysis of the effects of published features.

3 implementing Collins' model using only the published details causes an 11% increase in relative error over Collins' own published results.


3 In particular, implementing Collins' model using only the published details causes an 11% increase in relative error over Collins' own published results.

taken together, all the unpublished details have a significant effect on overall parsing performance.


Collins' thesis discusses a case in which the initial head is modified when it is found to denote the right conjunct in a coordinated phrase.

if the head rules pick out a head that is preceded by a CC that is non-initial, the head should be modified to be the nonterminal immediately to the left of the CC (see Figure 6).


An important detail is that such "head movement" does not occur inside base NPs.

a phrase headed by NPB may indeed look as though it constitutes a coordinated phrase-it has a CC that is noninitial but to the left of the currently chosen head-but the currently chosen head should remain chosen.


While this is not spelled out, as it happens, a verb is any word whose part-of-speech tag is one of {VB, VBD, VBG, VBN, VBP, VBZ}.

the cv predicate returns true only for these preterminals and false for all other preterminals.


Essentially, the model for generating children of NPB nodes is a "bigrams of nonterminals" model.

it looks a great deal like a bigram language model, except that the items being generated are not words, but lexicalized nonterminals.


Heads of NPB nodes are generated using the normal head-generation parameter, but modifiers are always generated conditioning not on the head, but on the previously generated modifier.

we modify expressions (7) and (8) to be Though it is not entirely spelled out in his thesis, Collins considers the previously generated modifier to be the head-child, for all intents and purposes.


Curiously, however, Collins' implementation considers the head label of the NP chart item not to be NPB, but rather the head label of the NPB chart item.

to get the head label of an NP chart item, one must "peek through" the NPB and get at the NPB's head label.


Number of times our parsing engine was able to deliver a probability for the various levels of back-off of the modifier-word generation model, P Mw , when testing on Section 00, having trained on Sections 02-21.

this table reports how often a context in the back-off chain of P Mw that was needed during decoding was observed in training.


However, Gildea (2001) reimplemented Collins' Model 1 (essentially Model 2 but without subcats) and altered the P Lw and P Rw parameters so that they no longer had the top level of context that included the headword (he removed back-off level 0, as depicted in Table 1).

gildea removed all bilexical statistics from the overall model.


These expectation maximization (EM) experiments were motivated by the desire to overcome the limitations imposed by the heuristics that have been heretofore used to perform head-lexicalization in treebanks.

it appeared that the head rules used in Collins' parser had been tweaked specifically for the English Penn Treebank.


The notation is that used throughout this article.

for notational brevity we use M(w, t) i to refer to the three items M i , t M i , and w M i that constitute some fully lexicalized modifying nonterminal and similarly M(t) i to refer to the two items M i and t M i that constitute some partially lexicalized modifying nonterminal.


The problems with history-based models and the desire to be able to specify features as arbitrary predicates of the entire tree have been noted before.

previous work (Ratnaparkhi, Roukos, and Ward 1994;Abney 1997;Della Pietra, Della Pietra, and Lafferty 1997;Johnson et al.


As far as we are aware, behavior of the models for finite sample sizes is less well understood.

while feature selection methods have often been proposed for maximum-entropy models, little theoretical justification (in terms of guarantees about generalization) has been given for them.


, m is taken to be a fixed set of relatively simple functions.

we have assumed that m is relatively small (for example, small enough for algorithms that require O(m) time or space to be feasible).


in some experiments in this article, we use the following definition: where, as defined in section 4.1, Scoreðx i, j Þ is some measure of the ''goodness'' of a parse, such as the F-measure (see section 5 for the exact definition of Score used in our experiments).


Next, the probabilities Pðr i j F i ,pÞ are combined with the probabilities Pðfr 1 : : : n gjpÞ for a set of roles appearing in a sentence given a predicate, using the following formula: Pðr 1 : : : n jF 1 : : : n , pÞ , Pðfr 1 : : : n gjpÞ Y i Pðr i jF i , pÞ Pðr i jpÞ This approach, described in more detail in Gildea and Jurafsky (2002), allows interaction among the role assignments for individual constituents while making certain independence assumptions necessary for efficient probability estimation.

we assume that sets of roles appear independent of their linear order and that the features F of a constituent are independent of other constituents' features given the constituent's role.


While treebank parsers such as that of Collins (1999) return much richer representations than a chunker, they do not include a great deal of the information present in the original Penn Treebank.

longdistance dependencies indicated by traces in the treebank are crucial for semantic interpretation but do not affect the constituent recall and precision metrics most often used to evaluate parsers and are not included in the output of the standard parsers.


Considering also that cond stands for the specific property being a nominal phrase (np) whose head denotes a legal document (abbreviated by doc), then the pair bbof_right, ratificationÀ, docÀ means that the particular position ratification of [_ ] selects for nouns denoting legal documents.

ratification requires nominal arguments denoting legal documents to appear after preposition of.


Another important objective of the article is to use the semantic requirements to capture contextually relevant semantic similarities between words.

we assume that two words filling the same semantic requirement share the same contextual word sense.


They are used to extensionally define the particular condition they can fill.

a condition is defined by identifying those words likely to appear in positions considered similar.


Information on word similarity is used to generalize the pairs appearing in the corpus and to smooth their co-occurrence probabilities.

very specific requirements described at the lexical level can be generalized by means of word similarity information.


Hard requirements can easily be used to constrain the grammar of a symbolic parser.

we use them to improve the parser described in Rocio, de la Clergerie, and Lopes (2001).


Within a particular corpus, we assume that the meaning of a word is defined by the context types that organize the different positions of the word.

a word's meaning is described by identifying the types of requirements the word fulfills.


By contrast, when it plays a more active role, behaving more like a Predicate, it is viewed as a modifier or the adjunct of the head (Figure 2).

the dependent of a head-dependent structure is described either as a passive complement, if it satisfies the linguistic requirements of the head (Argument role), or as an active modifier, when the dependent itself requires a specific type of head (Predicative role).


No linguistic proposal is able to distinguish in absolute terms complements from external adjuncts; for example, in the previous expression, is with a doll an internal complement or an adverbial modifier of play?

is position biobj_with_down,playÀ one that requires as argument the noun doll (complement construction)?


All binary dependencies always contain a certain degree of both complementarization and modification.

given a dependency, the head requires the dependent (complementarization), and conversely, the dependent requires the head (modification).


It is assumed here that two syntactically related expressions presuppose two complementary requirements.

every binary dependency is constituted by two compatible predicate-argument structures.


On the contrary, each word of a binary dependency is perceived simultaneously as both a predicate and an argument.

each word both imposes semantic requirements and matches semantic requirements in return.


Given dependency (robj; ratificar # , lei " ) (ratify the law), we compute the two following lexical associations: The scores of these two associations are taken into account in the evaluation procedure.

the sum of the two scores (if each of them is greater than the empirically set threshold) will be used to make a decision on the attachment between an np headed by lei and a vp headed by ratificar.


However, there are important differences among the precision values associated with the three phrase sequences.

the scores on sequence vp-np-pp are significatively higher than those on the other sequences, regardless of the method employed.


It then discusses how argumentation theory contributes to legal argument, and how AI-and-law systems have implemented the different stages of legal argumentation.

noteworthy is the detailed grid that shows the argumentation processes that have been implemented in existing AI-and-law systems.


However, it either dismisses or ignores quantitative and probabilistic approaches.

chapter 3 gives a somewhat ill-advised critique of decision calculi in the context of decision-support systems, and fails to mention anytime algorithms (Dean and Boddy 1988) in the context of reasoning under resource constraints.


Although this is computationally the easiest approach, the resulting PFA may be a very inaccurate approximation of the probability distribution described by the input PCFG.

there may be assignments of probabilities to the transitions of the same FA that lead to more accurate approximating language models.


But the explicit marking of barriers is somewhat against the original appealing TAG idea that such constraints result from imposition of the CETM, according to which the position of the moved element and the verb it depends on must be in the same elementary structure, and from the further combination possibilities of this structure.

in local formalisms with an extended domain of locality such as TAG or tree-local and set-local MCTAG, such constraints result from the form of the elementary structures and from the locality of the derivation operation.


In other words, in local formalisms with an extended domain of locality such as TAG or tree-local and set-local MCTAG, such constraints result from the form of the elementary structures and from the locality of the derivation operation.

they follow from general properties of the grammar, and they need not be stated explicitly.


It is replaced by the verspricht tree, and its daughters have become daughters of the foot node of the verspricht tree.

the root node of the derived tree is considered to belong only to the verspricht tree.


5 Therefore, we propose that the two nodes in question belong to both verspricht and reparieren.

these nodes are shared by the two elementary trees.


This is different in the case of shared nodes: After β 1 and then β 2 have been adjoined, the root node of β 2 in the derived tree is shared by β 1 and β 2 and consequently can be used for adjunctions at β 1 .

7 trees to be adjoined at the roots of β 1 and β 2 can be adjoined in any order.


But since these constraints need to hold only for the TAG derivation trees that correspond to derived trees in the tree language, subderivation trees need not satisfy them.

γ 1 and γ 2 from the same elementary tree set can be added at different moments of the derivation as long as the final complete TAG derivation tree satisfies (MC1)-(MC3).


For such an MCTAG, each TAG derivation tree is trivially SN-tree-local.

in other words, in this case the tree sets are the same, whether the grammar is considered a TAG, a tree-local MCTAG, or an  all TAG analyses proposed so far can be maintained, since each TAG is trivially also an instance of SN-MCTAG.


Furthermore, there are more predicate-argument dependencies: Joshi, Becker, and Rambow (2000) call the strong co-occurrence constraint.

the dependency tree should be as in Figure 6.


r The element intervenes between some element A (on its right) and some element B (on its left) scrambled out of A, and the element itself does not belong to A.

the fact that the set for scrambling is used for some element does not necessarily mean that this element is scrambled.


For an SN-derivation structure of a certain arity, the number of crossings of secondary edges inside a single secondary group is then limited: For an SN-derivation structure of arity n, the number of crossings of secondary edges per secondary group is limited to n 2 − 1.

if i is the maximal number of crossings, then 2(i + 1) is the arity of the grammar.


To us, the work we present here seems to be the crucial first step in avoiding arbitrary constraints on inferences for building discourse structures.

the point we wish to make here is that although there might be other constraints on possible discourse annotations that will have to be identified in future research, tree structure constraints do not seem to be the right kinds of constraints.


It is unclear how a dividing line could be drawn between tree structures and their augmentation.

it is unclear how one could decide which part of a text coherence structure should be represented by the tree structure and which part should be represented by the augmentation.


the procedure outlined in section 4.1.1).

the question is whether the frequency distribution over types of coherence relations is different for arcs participating in crossed dependencies compared to the overall frequency distribution over types of coherence relations in the whole database.


However, there is a possible alternative hypothesis to Knott's (1996).

elaboration relations are very frequent (37.97% of all coherence relations; cf.


It is possible that removing elaboration relations from the database reduces the number of crossed dependencies only because a large number of coherence relations are removed when elaborations are removed.

an alternative hypothesis to that of Knott (1996) is that the lower number of crossed dependencies is just due to lessdense coherence graphs (i.e., the less dense coherence graphs are, the lower the chance for crossed dependencies).


As with crossed dependencies, an important question is whether there are certain types of coherence relations that are more or less frequently ingoing to nodes with multiple parents than other types of coherence relations.

the question is whether the frequency distribution over types of coherence relations is different for arcs ingoing to nodes with multiple parents compared to the overall frequency distribution over types of coherence relations in the whole database.


Because trees are neither a descriptively adequate data structure for representing coherence structures nor easier to derive, we argue for less constrained graphs as a data structure for representing coherence structures.

we argue for a representation such as chain graphs (cf.


Language generation offers an appealing approach to the problem, but the use of generation in this context raises significant research challenges.

generation for sentence fusion must be able to operate in a domainindependent fashion, scalable to handle a large variety of input documents with various degrees of overlap.


In the multidocument scenario, lexical chains can be adapted for theme ranking based on the salience of theme sentences within their original documents.

a theme that has many sentences ranked high by lexical chains as important for a single-document summary is, in turn, given a higher salience score for the multidocument summary.


We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora and definite noun phrase coreference.

we compare an algorithm that relies on links encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora by means of shallow lexico-semantic patterns.


In Section 6 we discuss several aspects of our findings that still need elaboration in future work.

our work is purely comparative and regards the different lexical knowledge sources in isolation.


This happens when the Web score of an antecedent candidate that does not match the anaphor is higher than the Web scores of matching antecedent candidates.

there is no guarantee that matching antecedent candidates are included in A WM anaid .


As expected, the WordNet-based algorithms suffer from the problems discussed in Section 2.2.

problem 1 proved to be quite severe, as algoWN achieved a coverage of only 65.2%.


However, it is not clear how far the method and the achieved comparative results generalize to other kinds of full NP anaphora.

we are interested in the following questions: r Is the knowledge gap encountered in WordNet for other-anaphora equally severe for other kinds of full NP anaphora?


All algorithms we have described can be considered blueprints for more complex versions.

the WordNet-based algorithms could be improved by exploiting information encoded in WordNet beyond explicitly encoded links (glosses could be mined, too, for example; see also Harabagiu, Bunescu, and Maiorano [2001]).


We have explored two different ways of exploiting lexical knowledge for antecedent selection in other-anaphora and definite NP coreference.

we have compared a hand-crafted and -structured source of information such as WordNet and a simple and inexpensive pattern-based method operating on corpora.


Moor claimed that passing the Turing Test is evidence of intelligence; the test is neither a definition nor a sufficient condition of intelligence.

it is good inductive "evidence for Block's .


For an audience interested in more-practical methods that are accompanied by thorough evaluations, I recommend chapters 5, 17, and 18.

chapter 5 (Ralph Weischedel, Jinxi Xu, and Ana Licuanan) takes a look at answering biographical questions.


What he said was, "If you want to study French, don't you want to study it as used by those who use it best?"

would I not want to study the French of the literary giants who created great works of art in that language?


The parsing algorithm consisted in moving phrases and partial phrases from the agenda to the chart in such a way as to maintain this invariant.

repeat the following cycle until the agenda is empty: Remove an arbitrary partial or complete phrase from the agenda.


If the register is still empty, you can put something in it, but once it has a value, that value must stay there.

registers must be variables, in the mathematical understanding of that term, and not Fortran variables that can be assigned and reassigned indefinitely.


Regular relations, which are modeled by finite-state transducers and are very closely related to finite-state machines of the standard kind, have just the power that is needed for many linguistic operations, particularly at the low end of the hierarchyphonology, morphology, and spelling rules.

as Ron and I pointed out (Kay and Kaplan 1994), a slight adjustment to the rules of engagement, as they are called in military circles these days, moves simple string-rewriting rules right from the top of the Chomsky hierarchy to the bottom or, at least, one step from the bottom.


Thus, although in general the scores computed by each will be different, the orderings or rankings of objects will be the same.

for all k and w, the k nearest neighbors of word w according to Jaccard's Coefficient will be identical to the k nearest neighbors of word w according to the Dice Coefficient and the harmonic mean of precision and recall in the additive type-based CRM.


Thus the L 1 distance between two words, w 1 and w 2 , can be written as: However, noting the algebraic equivalence A + B − |A − B| ≡ 2 • min(A, B) and using basic probability theory, we can rewrite the L 1 Norm as follows: However, min(P(c|w 1 ), P(c|w 2 )) > 0 if and only if c ∈ TP.

hence: the L 1 Norm is directly related to the difference-weighted tokenbased CRM.


First, for high-and low-frequency nouns, similarity with WordNet is higher for low values of β than for high values of β.

neighbors according to the WordNet based measure tend to have high-recall retrieval of the target noun's co-occurrences.


This indicates that a potential neighbor with high-precision retrieval of informative features is more useful than one with high-recall retrieval.

it seems that it is better to sacrifice being able to make decisions on every test instance with a small number of neighbors in favor of not having neighbors that predict incorrect verb co-occurrences.


Second, there are other potential application-based tasks that could be used to evaluate CRMs and distributional similarity methods in general.

we see potential for the use of distributional similarity methods in prepositional phrase attachment ambiguity resolution.


In Knight and Marcu's data, we found that from a corpus of 39,060 abstract sentences, only 1,067 were created from corresponding document sentences via deletion of contiguous segments.

only 2.7% of the sentences in real document, abstract pairs can be explained by the model proposed by Knight and Marcu (2002).


After our discussion of the model structure and algorithms, we discuss the various parameterizations we employ in Section 4.

we discuss three distinct models of movement, two of which are well-known in the machine translation alignment literature, and a third one that exploits syntax in a novel, "light" manner.


Finally, we investigated the issue of adjacency in the alignments.

we consider the following question: Given that a summary phrase ending at position i is aligned to a document phrase ending at position j, what is a likely position in the document for the summary phrase beginning at position i + 1?


In order to make this computationally tractable, we must introduce some conditional independence assumptions.

we assume the following: Decision (a) in our generative story depends only on the position of the end of the current document phrase (i.e., j + l).


There is a unique initial state start , a unique final state end , and a state for each possible document phrase.

for a document of length n, for all 1 ≤ i ≤ i ≤ n, there is a state that corresponds to the document phrase beginning at position i and ending at position i , which we will refer to as r i,i .


It is straightforward to estimate these parameters based on the estimations from the forward-backward algorithm.

jump rel (i) is simply the relative frequency of length i jumps, and jump rel (∅) is simply the count of jumps that end in a null state to the total number of jumps.


The Gaussian jump model attempts to alleviate the sparsity of data problem in the relative jump model by assuming a parametric form to the jumps.

we assume there is a mean jump length µ and a jump variance σ 2 , and then the probability of a jump of length i is given by: Some care must be taken in employing this model, since the normal distribution is defined over a continuous space.


To formally capture this notion, we parameterize the syntax-aware jump model according to the types of phrases being jumped over.

to jump over tripled it 's sales would have probability jump syn (VBD PRP POS NNS) while to jump over of Apple Macintosh systems would have probability jump syn (PP).


The form of the word identity rewrite model is: rewrite id (s | d) = δ s=d .

the probability is 1 exactly when s and d are identical, and 0 when they differ.


One problem with these definitions is that phrase-based models are fond of making phrases.

when given an abstract containing the man and a document also containing the man, a human will align the to the and man to man.


Therefore, in addition to the dictionary, many systems also contain special components for unknown word identification.

statistical methods have been widely applied because they use a probabilistic or cost-based scoring mechanism rather than a dictionary to segment the text.


As shown in Table 1, there are five main categories of morphological processes, each of which has several subcategories, as detailed in Figure 2 (see Wu [2003] for a detailed description): r Affixation (MP and MS): r Splitting (MS) (i.e., a set of expressions that are separate words at the syntactic level but single words at the semantic level): £ Ñ 'already ate', where the bi-character word £Ñ 'eat' is split by the particle 'already'; r Merging (MM): ª 'on duty' + « 'off duty' → ª« 'on-off duty'; and r Head Particle (MHP) (i.e., expressions that are verb + comp): The second characteristic of MDWs is that they form stable Chinese character sequences in the corpus.

the components within the MDWs are strongly correlated (of high co-occurrence frequency), while the components at both ends have low correlations with words outside the sequence.


NWs (new words): NWs are OOV words that are neither recognized as named entities or factoids nor derived by morphological rules.

we focus on lowfrequency new words, including newly coined words, occasional words, and mostly time-sensitive words (Wu and Jiang 2000).


The complexity has to be controlled manually.

all new added rules are recompiled by a linguist so that the total number of rules is manageable.


The second is due to the fact  that we currently do not know any effective way of using multiple segmentations in the above-mentioned applications.

we segment each sentence as much as possible into words that are stored in the reference lexicon.


Notice that both the LW and MDW classes are open sets and that we need to assign a floor-value to those words that are not stored in the lexicons.

we define six unknown word classes as follows.


Class model parameters are updated in a simple additive fashion: Parameters are altered according to the gradient with respect to MSELoss i (λ λ λ).

in our implementation, we approximate w R as the w in GEN(s) with the fewest errors, so Score(λ λ λ, s, w R ) − Score(λ λ λ, s, w) ≤ 0, where the equality holds only when w R = w. the model parameters are updated when the sentence is wrongly segmented.


We then assume that the sum is approximated by a single pair of terms P(w * | ON)P(s | w * ), where w * is the most probable word class segmentation discovered by Equation 5.

we also use our system to find w*, but the source-channel models are estimated on the ON list.


Then, given an FN candidate, three named entity candidates, each for one category, are generated in the lattice, with the class probabilities P(s|PN) = P(s|LN) = P(s|ON) = P(s|FN).

we postpone the determination of its type to the decoding phase where the context model is used.


Each feature function is a mapping f (s, w) ∈ .

let TF1(s, w) (or TF2(s, w)) be the term frequency of w in the case that s is a 1-word (or 2-word) string.


We also did not differentiate LNs and ONs in evaluation.

we only checked the word boundaries of LNs and ONs and treated both tags as interchangeable.


For example, MSRSeg achieves better average performance than the other three segmenters that report results on all four data sets (i.e., S03, S11, CRF).

mSRSeg outperforms them on every data set.


The program committee as a whole is evaluated in terms of precision and recall.

if they accept a papers with the best votes, how well do those papers match the gold standard?


They suggest that if there was an "instance of" relation, they could distinguish between a concept-to-concept relation of subsumption and an individual-to-concept relation of instantiation.

essentially, the suggestion we follow in the present work, but in some cases the distinction was not easy to draw.


r Given any formula rel(c 1 , c 2 ) for semantic relatedness between two concepts c 1 and c 2 , the relatedness rel(w 1 , w 2 ) between two words w 1 and w 2 can be calculated as where s(w i ) is "the set of concepts in the taxonomy that are senses of word w i " (Resnik 1995).

the relatedness of two words is equal to that of the most-related pair of concepts that they denote.


As a consequence, the higher the position of the most-specific subsumer for given two concepts in the taxonomy (i.e., the more abstract it is), the lower their similarity.

if the taxonomy has a unique top node, its probability will be 1, so if the most-specific subsumer of a pair of concepts is the top node, their similarity will be − log(1) = 0, as desired.


In contrast, two other measures, Resnik's and Lin's, behave quite similarly to each other in the low-similarity region.

their sets of zero-similarity pairs are identical, because the definitions of both measures include the term log p(lso(c 1 , c 2 )), which is zero for the pairs in question.


Naturally occurring coherent texts, by their nature, contain many instances of related pairs of words (Halliday and Hasan 1976;Morris and Hirst 1991;Hoey 1991;Morris and Hirst 2004).

they implicitly contain human judgments of relatedness that we could use in the evaluation of our relatedness measures.


The most significant difference, however, arose from the limitations due to data sparseness that are inherent in methods based on distributional similarity: the very small size of the set of words that could be corrected.

only malapropisms for which both the error and the correction occurred in the set of 2,000 words for which Weeds had distributional data could be considered; and the ability to detect and correct the malapropism depended on other members of that set also being within the scope of the target word.


Obviously, however, such an automaton over-generates by accepting also invalid words such as m ut.

it ignores the dependencies which hold between prefixes and suffixes of the same circumfix.


The number of configurations in A is |Q| × |Γ| n , hence the growth in the number of states when constructing A from A might be in the worst case exponential in the number of registers.

the move from FSAs to FSRAs can yield an exponential reduction in the size of the network.


Clearly, FSRAs provide a significant reduction in the network size.

we could not construct an n-bit incrementor FSA for any n greater than 100 as a result of memory problems, whereas using FSRAs we had no problem constructing networks even for n = 50, 000.


In order for finite-state networks to be useful for linguistic processing, we provide a regular expression language denoting FSRAs.

we provide a set of extended regular expression operators that denote FSRAs and FSRTs.


A useful conversion of FSAs to FSRAs must minimize some combination of these parameters, and while it may be intractable in general, it can be practical in many special cases.

the case of finite languages (acyclic FSAs) is both of practical importance and -we conjecture -can result in good compaction.


More work is also needed in order to establish more properties of FSRTs.

we did not address issues such as sequentiality or sequentiability for this model.


One of the striking results in quantum theory is that this can be done consistently and uniquely (Gleason 1957).

von Neumann was well aware of this although he could not prove it; in 1954, he said: probability corresponds precisely to introducing the angles geometrically.


For example, the accuracy/purity measure (Stevenson and Joanis 2003;Korhonen, Krymolowski, and Marx 2003) evaluates whether a verb is assigned to a correct cluster with respect to the gold standard class of the majority of cluster members.

in a first step each induced verb cluster is assigned a gold standard class according to which class captures the majority of the cluster members.


Then IA would tend to have little use for this property at all since, presumably, every chihuahua would be small and every alsatian large, making each of the combinations {large, chihuahua} (which denotes the empty set) and {large, alsatian} (the set of all alsatians) useless.

existing treatments of gradables in GRE fail to take the "efficiency of language" into account (Barwise and Perry 1983; see our Section 2).


Thus, gradable GRE takes only polynomial time, and if we focus on the part that cannot be done off-line, it takes only linear time.

gradable GRE does take more time than nongradable GRE, but the difference seems modest.


The outcomes of the experiment suggested that readers understand plural vague descriptions in accordance with the semantics of Section 2 (van Deemter 2000).

they judged the description to be correct if and only if the two highest numbers in the sequence appeared in brackets.


CD for this type of descriptions along the lines of Section 4 is not difficult once relational descriptions are integrated with a standard GRE algorithm (Krahmer and Theune 2002, Section 8.6.2): Suppose an initial description is generated describing the set of all those dogs that are in sheds over a given size (say, size 5); if this description happens to distinguish an individual dog then this legitimizes the use of the noun phrase the dog in the large shed.

note that this is felicitous even if the shed is not the largest one in the domain, as is true for d 2 in the following situation (contains-a=b means that a is contained by b): the dog in the large shed denotes 'the dog such that there is no other shed that is equally large or larger and that contains a dog'.


Size: d1 (20 cm), d2 (50 cm), d3 (70 cm), d4 (60 cm), c5 (50 cm) If we are interested in the three most salient dogs (d 1 , d 2 , and d 4 ) then the large(est) dog designates d4, but if we are interested in the four most salient ones (d 1 , d 2 , d 3 , and d 4 ), then it designates d 3 , for example.

the description is ambiguous between d3 and d4, depending on whether we attach greater importance to salience or size.


Here, at each step, a single predictor is eliminated from the current regression model if its removal would increase the sum-of-squares differences between the observed and the predicted values, by not more than F out (some properly chosen constant, typically between 2 and 4) times the residual mean square RSS n−p , where n is the number of observations and p is the number of predictors.

the predictor giving the smallest increase in RSS is chosen.


The conversion of Web pages into ASCII represents a potential source for new errors.

the conversion of German PDF documents to ASCII turned out to be very error prone.


This is, however, a simplification of the problem, which does not take the nature of the attachment into account.

it does not distinguish PP arguments from PP adjuncts.


We propose a methodology that retains both the linguistic insight of the grammatical tests and the ability to effectively combine several gradient, partial diagnostics, typical of automatic induction methods.

we first find countable diagnostics for the argument-adjunct distinction, which we approximate statistically and estimate using corpus counts.


Here a given verb may tolerate the omission of its argument.

a transitive verb, such as kiss, can also act like an intransitive.


The problem with this solution is that the training data S Tr is no longer extracted using the same process as the testing data S Te , and is therefore not good data from which to generalize.

all tuples in the training data S Tr necessarily also occur in the statistics corpus C St , and therefore no vectors in the training data S Tr involve data unseen in the statistics corpus.


We addressed this problem by using a backed-off estimation, where we replace lexical items by their WordNet classes and collect counts on this representation.

the iterativity measure has been implemented as follows.


Clearly, although both kinds of PPs are well identified, arguments are better identified than adjuncts, an observation already made by several other authors, especially Hindle and Rooth (1993) in their detailed discussion of the errors in a noun or verb PP-attachment task.

we notice that more adjuncts are misclassified as arguments than vice versa.


The reported results are good enough to be practically useful.

the distinction between arguments and adjuncts attached to nouns is probably performed as well as possible with an automatic method, even by simply using prepositions as features.


The data for the experiments illustrated below are drawn from the same corpora as those used in the previous experiments.

recall that the corpus from which we draw the statistics is different from the corpus from which we draw the training examples and also from the testing corpus.


If X is of rank r, then Σ is also of rank r. Let Σ k , where k < r, be the diagonal matrix formed from the top k singular values, and let U k and V k be the matrices produced by selecting the corresponding columns from U and V. The matrix U k Σ k V T k is the matrix of rank k that best approximates the original matrix X, in the sense that it minimizes the approximation errors.

,X = U k Σ k V T k minimizes X − X F over all matricesX of rank k, where .


In general (except when the semantic relations between A and B are symmetrical), we have the following inequality: sim r (A : B, C : D) = sim r (B : A, C : D) Therefore we do not want A:B and B:A to be represented by identical row vectors, although it would ensure that equation 8is satisfied.

in step 12 of LRA, the relational similarity between A:B and C:D is the average of the cosines, among the (num filter + 1) 2 cosines from step 11, that are greater than or equal to the cosine of the original pairs, A:B and C:D. the average includes only those alternates that are "better" than the originals.


The precision and recall are significantly below the baseline LRA until N ≥ 300 (95% confidence, Fisher Exact Test).

for a typical SAT analogy question, we need to examine the top 300 patterns to explain why LRA selected one choice instead of another.


There is growing evidence that there is much variance in the way people react to system errors and that the variance can be explained on the basis of particular properties of the dialogue system or the dialogue context.

dialogue confirmation strategies may hinder users' ability to correct system error.


Table 7 shows that, using only non-prosodic features (ASR, SYS, POS), corrections can still be predicted with an accuracy statistically equivalent to our best results.

using all feature types (PROS+ASR+SYS+POS, DIA = all, error = 16.38%) is equivalent to using only non-prosodic features (ASR+SYS+POS, DIA = all, error = 17.01%).


This finding is an important one because it suggests that systems that have access to restricted kinds of information can still hope to identify user corrections with some confidence.

simply using information available to current ASR systems, such as acoustic confidence score, recognized string, grammar, and features derived from these, produces classification results equivalent to our best-performing classifier.


Also, in Sections 3.1 and 3.4, we identified differences between different types of corrections, which suggests that our features might be more effectively used to predict each correction type differently.

what would happen if instead of predicting whether a turn was a correction (T) or not (F) (the binary classification task investigated above), we predicted whether a turn was ADD, ADD/OMIT, OMIT, PAR, REP, or F (i.e., not a correction).


Generalization theory asks the question of how well a learner classifies unseen data given only a limited amount of training data, instead of referring to the law of large numbers for guarantees in parameter estimation.

it asks how much training data is needed for an estimator to converge to a point where it has minimal error on unseen data, that is, where it is probably approximately correct.


As the freshest example, let us only refer to the recent EACL 2006 conference in Trento and the HLT-NAACL 2006 conference in New York with its CoNLL-X Shared Task on Multilingual Dependency Parsing (working with treebanks of 12 languages, of different sizes).

the seemingly surface oriented analysis is prevailingly dependency based.


There are two interesting points in his approach: First, Chomsky connects these syntactic issues with the placement of the intonation center in the spoken form of the sentence, and second, he connects the possible operational criterion for the determination of the choice of focus from the range of permissible focus with the scope of negation.

to decide what is the focus of the answer to Was it an ex-convict with a red SHIRT that he was warned to look out for?, one should consider possible different negative continuations such as No, he was warned to look out for an AUTOMOBILE SALESMAN, or .


We have tried to demonstrate on certain selected grammatical and discourse phenomena (in Hajičová, Sgall 2006) that the process of the annotation during the last decade and its results have allowed for an enrichment of this framework in several regards.

our examples were taken from the domain of the condition of projectivity, classification of dependency relations, topic-focus articulation (the bipartition of the sentence into topic and focus and the cannonical underlying word order in the focus of the sentence), and some aspects of discourse structure.


Kendall's τ can be interpreted as a simple function of the probability of observing concordant and discordant pairs (Kerridge 1975).

it is the difference between the probability that in the observed data two variables are in the same order versus the probability that they are in different orders (the probability is rescaled to range from −1 to 1 as is customary for correlation; see equation 1).


For each text we randomly selected three synthetically generated orders, each from Bin 1 (high τ value), Bins 2-4 (medium τ value), and Bins 5-8 (low τ value).

we collapsed Bins 2-4 and Bins 5-8, since the ANOVA revealed that ratings for these bins were not significantly different.


As can be observed in Table 5, the inclusion of the four feature functions into the translation system definitively produces a significant improvement in translation quality in both translation directions.

it becomes evident that the features with the most impact on translation quality are the lexicon models.


In fact, this outcome is a general finding for nearly all of the TREC tasks discussed in this book.

standard statistical IR methods have proven to be quite capable of performing in a resilient way on different types of data and in many different retrieval scenarios.


The word posterior probabilities introduced in Section 3.2 can be interpreted as the probability of a word being correct.

the probability can directly be used as a confidence measure.


In such a word graph, the first word of a target phrase is assigned the score for the whole phrase.

when translating a source phrasef k by a target phraseẽ k = e i k−1 +1 .


The tasks set in these conferences have molded a specific kind of question answering that is easy to evaluate and that focuses on the use of fast and shallow methods that are generally independent of the application domain.

current research focuses on text-based, open-domain question answering.


Both systems were very successful in their chosen domains.

lUNAR was demonstrated at a lunar science convention in 1971, where it was able to answer 90% of questions posed by geologists without prior instructions with regard to the allowable phrasing (Hirschman and Gaizauskas 2001).


These queries took advantage of existing advanced search features to simulate the types of results that would be currently available to a knowledgeable physician.

widely accepted tools for narrowing down PubMed search results such as Clinical Queries were employed whenever appropriate.


Our results suggest that our target users (medical researchers) can quickly learn to construct queries of the type and complexity that they have identified as relevant.

: r They are able to use the Conceptual Authoring method successfully to compose complex queries, with no prior exposure to the method and with the benefit of only minimal training.


Chapters 4 through 6 clearly complement one another, although identifying overlapping or related sections often requires some effort on behalf of the reader.

i often found it hard to keep track of literature reviewed in different chapters.


Although the various default unification operators differ in their particulars, most involve something like Carpenter (1992)'s credulous default unification as one step.

the result of credulously adding the default information in G to the strict information in F is: cred-unify (F, G) is the result of unifying F with the maximal consistent subset(s) of the atomic constraints in G. A subset of constraints is maximally consistent with respect to the subsumption ordering if no constraint can be added to it without creating an inconsistency.


We next consider the problem of recognizing synonyms by selecting an appropriate synonym for a target word from a set of semantically related candidate words.

we evaluate the performance of our model on synonym questions from the Test of English as a Foreign Language (TOEFL).


r medium contexts add to minimum contexts dependency paths which model the internal structure of noun phrases (length ≤ 3; 59 templates).

the medium context covers phenomena such as coordination, genitive constructions, noun compounds, and different kinds of modification.


r gram-rel (v gram-rel , see Equation (12)) uses the obliqueness hierarchy (Keenan and Comrie 1977) to rank paths according to the salience of their grammatical relations.

each path is assigned the value of its most salient grammatical relation (subjects are more salient than objects, which are more salient than other noun phrases).


In order to allow a fair comparison, we trained the word-based model on the same corpus as the dependency-based model (the complete BNC) and selected parameters that have been considered "optimal" in the literature (Patel, Bullinaria, and Levy 1998;Lowe and McDonald 2000;McDonald 2000).

we built a word-based model with a symmetric 10 word window as context and the most frequent 500 content words from the BNC as dimensions.


The Prime effect size was greater for the dependency model, which obtained an η 2 of 0.332 compared to the word-based model whose η 2 was 0.284.

the dependency model accounted for 33.2% of the variance, whereas the word-based model accounted for 28.4%.


Instead, we assessed the performance of these models on the BNC, using a search engine which simulated AltaVista.

we indexed the BNC using Glimpse (Manber and Wu 1994), a fast and flexible indexing and query system.


Our model's mistakes are often "near misses", that is, the first and second sense ranks are flipped.

near misses are observed for 25% of the noun types in Band 1,000-5,000, and 15% in Band 5,000+.


To avoid this, we filter our grammar according to the following constraints, chosen to balance grammar size and performance on our development set: If there are multiple initial phrase pairs containing the same set of alignments, only the smallest is kept.

unaligned words are not allowed at the edges of phrases.


The other baseline, which we call Hiero Monotone, is the same as Hiero except with the limitation that extracted rules cannot have any nonterminal symbols on their righthand sides.

only conventional phrases can be extracted, of length up to 5.


Then the Incremental Algorithm starts selecting TYPE = library, followed by ROOM NUMBER = 120, at which stage a distinguishing description is reached.

the same description would be generated by this algorithm as by Dale and Haddock (1991) and, once, again, the infamous LO and DE would occur.


Where logically redundant information does not make the referent easier to identify, the results of the experiment are less clear, with the subjects being divided between logically minimal and logically redundant descriptions.

while supporting the informal observations reported in Sections 2 and 3, the experiment does not point to a generic preference of one of the two GRE algorithms presented in Section 4.


In attempt to quantify this insight, I have analyzed citations from papers published in Computational Linguistics in 1995 and in 2005.

i extracted all citations from Computational Linguistics (CL) articles (excluding book reviews) in these years to journal papers.


The citation pattern from 2005 NLE papers is overall quite similar to the citation pattern from 2005 CL papers.

looking at references to other language research communities, 2005 NLE papers have a few more references than 2005 CL papers to papers in psychology journals (3% vs. 1%) and HCI journals (2% vs. 0%), but on the other hand have fewer references to papers in linguistic journals (5% vs. 8%).


When I organized this Choosing words in computer-generated weather information presentation, idiolect forecasts Semiotic schemas: A framework for grounding situated language, cross-modal language in action and perception The emergence of compositional structures in language evolution, grammar induction perceptually grounded language games special issue, I originally suggested having it in Computational Linguistics, but I switched to Artificial Intelligence because many potential authors told me that they had no interest in publishing in Computational Linguistics, because it was not a journal that they and their research colleagues read.

they did not see Computational Linguistics as a venue that published papers about their kind of research.


In the recent literature, the alignment task has frequently been decoupled from the translation task and assumptions have been made about measuring alignment quality for machine translation which, it turns out, are not justified.

none of the tens of papers published over the last five years has shown that significant decreases in alignment error rate (AER) result in significant increases in translation performance.


The estimation task is straightforward if we ignore the margins.

we simply scale up the sample in the obvious way:â MF = a s D D s .


under naive sampling over postings, one could estimate the associations by 3 Suppose there are m defectives among N objects.


With "sampling over documents," it is convenient to express the sampling rate in terms of D s and D, whereas with sketches, it is convenient to express the sampling rate in terms of k and f .

the following two approximations allow us to flip back and forth between the two views: using sketches with size k, the corresponding sample size D s in "sampling over documents" would be D s ≈ D f k, where D f represents the data sparsity.


For example, suppose we need to estimate the number of documents containing the first two words.

we need to estimate the inner product between P 1 and P 2 , denoted by a (1,2) .


It will be shown, both theoretically and empirically, that our proposed method reduces the mean square error (MSE) by about 50%.

the proposed method achieves the same accuracy with about half the sample size (work).


we follow the suggestion in Ravichandran, Pantel, and Hovy (2005) of reporting the percentage of agreements in the top-S. we compare the top-S pairs based on a sample with the top-S pairs based on the entire data set.


It is reassuring that agreement rates remain fairly high (≈ 70%), even when we push the sampling rate way down (0.003).

we can find many of the most obvious associations with very little work.


The multivariate hypergeometric distribution can be simplified to a multinomial assuming "sample-with-replacement," which is often a good approximation when D s D is small.

according to the multinomial model, an estimator and its variance would be: for the margin-free model, the "sample-with-replacement" simplification still results in the same estimator but slightly overestimates the variance.


The idea is to assume "sample-with-replacement" and that one can identify a s from K 1 without knowledge of K 2 .

we assume a (1) is a product of two binomials: Setting the first derivative of the logarithm of (25) to be zero, we obtain which is quadratic in a and has a convenient closed-form solution: The second root can be ignored because it is always out of range: The evaluation in Section 4 showed thatâ MLE,a is close toâ MLE .


Furthermore, assuming "sample-with-replacement," we can apply the large sample theory 5 (Lehmann and Casella 1998, Theorem 6.3.10), which says thatâ MLE is asymptotically unbiased and converges in distribution to a Normal with mean a and variance 1 I(a) , where I(a), the expected Fisher Information, is where we evaluate E(a For "sampling-without-replacement," we correct the asymptotic variance 1 I(a) by multiplying by the finite population correction factor 1 − D s D : Comparing (17) with (29), we know that Var (â MLE |D s ) < Var (â MF |D s ), and the difference could be substantial.

when we know the margins, we ought to use them.


The bias of h(â MLE ) decreases with sample size.

the delta method (Agresti 2002, Chapter 3.1.5) says that h(â MLE ) is asymptotically unbiased and the variance of provided h (a) exists and is non-zero.


our previous argument based on coefficient of variations (cv) is closely related to (42).


Suppose we can compute the maximum allowed total samples, T, for example, based on the available memory.

n i=1 k i = T, where n is the total number of words.


Firstly, Broder's estimator (80) uses k out of 2 × k samples.

it uses only intersections, which is always smaller than a s = |K 1 ∩ K 2 | available in the samples.


These constituents are obtained by type-raising and composing the arguments in each conjunct, yielding a functor which takes a verb with the appropriate category to its left to yield a verb phrase (Dowty 1988;Steedman 1985).

then the argument clusters are conjoined, and combine with the verb via function application: 21 this construction is one in which the CCGbank head-dependency structure (shown subsequently) fails to capture the full set of predicate-argument structure relations that would be implicit in a full logical form: ,3,$ , pay,((S[b]\NP)/NP)/NP,3,amounts , (50) away,(S\NP)\(S\NP),2,pay , in,((S\NP)\(S\NP))/NP,2,pay the dependency structure does not express the fact that right away takes scope over $5 million and in future over additional amounts, rather than the other way around.


These features (quant, deictic, proper n, pro, def desc, wh, and overt) are not annotated independently, but conditionally on the sluice type.

they will take yes as a value if the element or construction in question appears in the antecedent and it matches the semantic restrictions imposed by the sluice type.


A small set of features that capture the contextual properties that are relevant for NSU classification was identified.

three types of properties that play an important role in the classification task were singled out.


Whether or not the linguists could learn from us, does this pervasive Chomskyan flavor stop us learning beneficially from them?

does it stop us learning not just from the detailed data they may present, but from their theories and models, giving us necessary underpinnings for our applications?


However, on the specific reading must gets an epistemic interpretation.

we have made an inference about the age of the girl in question from her looks or other evidence.


This means that the current evaluation methodology for PP attachment does not produce realistic performance numbers.

it is not clear whether reattachment methods improve the parses of state-of-the-art parsers.


In CCGbank there is a distinction between the direct object of a transitive verb and ditransitive verb, for example, whereas in DepBank these would both be dobj.

to get a dependency correct in the CCGbank evaluation, the lexical category-typically a subcategorization frame-has to be correct.


Another contribution of the article is the development of log-linear parsing models for CCG.

we have shown how to define a CCG parsing model which exploits all derivations, including nonstandard derivations.


We look at a number of different data sources and parameterizations of the method, using evaluation results and error analyses to identify where the method performs well and also where it does not.

we find that the method does not work as well for verbs and adverbs as nouns and adjectives, but produces more accurate predominant sense information than the widely used SemCor corpus for nouns with low coverage in that corpus.


However, the study of Weeds and Weir (2005) provides interesting insights into what makes a "good" distributional similarity measure in the contexts of semantic similarity prediction and language modeling.

weighting features by pointwise mutual information (Church and Hanks 1989) appears to be beneficial.


So, for example, we ignore instances like the following with multiple wnsn values: <wf cmd=done pos=NN lemma=tooth wnsn=3;1 lexsn=1:05:02::;1:08:00::>tooth</wf> We also only evaluate on polysemous words (according to WordNet) having one sense in SemCor which is more frequent than any other, and for which both SemCor and our thesauruses have at least a minimal amount of data.

a word must occur three or more times in SemCor; it must also occur in ten or more grammatical relations in the parsed version of the BNC and have neighbors in the distributional thesaurus, or be present in Dekang Lin's thesaurus.


31 r For low frequency nouns in SemCor, the method, using any combination of automatically acquired thesaurus and semantic similarity score that we tried, produces more accurate predominant sense information than SemCor.

for nouns with a frequency of five or less (12.9% of the polysemous nouns in the Senseval-2 data) it outperforms the SemCor first sense heuristic.


In the second part of Chapter 2, they argue that there are many phenomena captured in these analyses that would have been missed by a model imposing a strict tree structure.

their analyses reveal a large number of crossed dependencies, and a large number of nodes participating in more than one relation, neither of which can be accounted for with a tree-based model.


We therefore consider an additional approach to entity extraction where entity classes are constructed simply by clustering nouns on the basis of their identity.

each noun in a text corresponds to a different entity in a grid, and two nouns are considered coreferent only if they are identical.


We assess whether differences in accuracy are statistically significant using a Fisher Sign Test.

we compare the full model against each of the less expressive models (see Table 5).


This variation can be explained by differences in entity realization between the two domains.

the two corpora vary in the amount of coreference they employ; texts from the Earthquakes corpus contain many examples of referring expressions that our simple identity-based approach cannot possibly resolve.


Addition of this knowledge source to our grid representation, doubles the number of features that serve as input to the learning algorithm.

salienceaware models need to learn twice as many parameters as salience-free models, while having access to the same amount of training data.


(2) How domain sensitive are the entity-grid models?

does their performance degrade gracefully when applied to out-of-domain texts?


To give a simple example, some domains will have more entities than others (e.g., descriptive texts).

entity transitions capture not only text coherence properties, but also reflect stylistic and genre-specific discourse properties.


Here, we move away from the coherence rating task and put the entity-grid representation further to the test by examining whether it can be usefully employed in style classification.

we embed our entity grids into a system that assesses document readability.


An advantage of maximum entropy models is that feature functions can represent any characteristics of events.

independence assumptions are unnecessary for the design of feature functions.


We also assume that terminal nodes of feature forests are conjunctive nodes.

disjunctive nodes must have daughters (i.e., γ(d) = ∅ for all d ∈ D).


Outside denotes a set of partial trees that derive node c 2 .

outside trees are partial trees of complements of inside trees.


Locality: In each step of composition of a predicate-argument structure, only a limited depth of the daughters' predicate-argument structures are referred to.

local structures in the deep descendent phrases may be ignored to construct larger phrases.


Monotonicity: All relations in the daughters' predicate-argument structures are percolated to the mother.

none of the predicate-argument relations in the daughter phrases disappear in the mother.


As defined in Section 5.1, "covered" indicates that the HPSG lexicon has all correct lexical entries for a sentence.

for covered sentences, exactly correct parse trees are obtained if the disambiguation model worked perfectly.


Furthermore, because feature forests are a generalized representation of ambiguous structures, each node in a feature forest need not correspond to a node in a PCFG parse forest.

a node in a feature forest may represent any linguistic entity, including a fragment of a syntactic structure, a semantic relation, or other sentence-level information.


This indicates that there is a trade-off between the locality of features and the cost of estimation.

larger context features might contribute to higher accuracy, while they inflate the size of feature forests and increase the cost of parameter estimation.


If coverage is a significant component in the performance difference observed between the hand-crafted and treebank-induced resources, then it is reasonable to expect that the performance difference is more pronounced with increasing sentence length (with shorter sentences being simpler and more likely to be within the coverage of the hand-crafted grammars).

we expect the hand-crafted, deep, precision grammars to do better on shorter sentences (more likely to be within their coverage), whereas the treebank-induced grammars should show better performance on longer strings (less likely to be within the coverage of the hand-crafted grammars).


Second, the treebank-and machine-learning-based LFG resources require automatic mapping to relate f-structure output of the treebank-based parsing systems to the representation format in the PARC 700 and CBS 500 Dependency Banks.

these mappings are partial and lossy: they do not cover all of the systematic differences between f-structure and dependency bank representations and introduce a certain amount of error in what they are designed to capture, that is they both overand undergeneralize, again adversely affecting scores.


Text mining is a highly interdisciplinary research area, bringing together research insights from the fields of data mining, natural language processing, machine learning, and information retrieval.

text mining is closely related to the older area of data mining, which targets the extraction of interesting information from data records, although text mining is allegedly more difficult, as the source data consists of unstructured collections of documents rather than structured databases.


Although the wide scope of this book is in many ways what makes it attractive, it also leads to some of its weaknesses.

the way INC is presented in this broad context did not feel optimal to me.


Another presentational issue concerns the tight integration of locality and incrementality in the book's definitions.

the virtual identification of incremental algorithms with computation on a local context makes one question why the book speaks of incremental rather than local algorithms.


These are performance on argument spans which were also guessed to be argument spans (but possibly the exact label was wrong).

these measures ignore the ARG vs. NONE confusions.


Thus, to improve the performance on CORE arguments, we need to improve recall without lowering precision.

when the model is uncertain which of several likely CORE labels to assign, we need to find additional sources of evidence to improve its confidence.


All features included in the local models are also included in our joint models.

each template for local features is included as a joint template that concatenates the local template and the node label.


This is because the two models will in fact be in the same parametric family but will only differ slightly in the way the parameters are estimated.

the likelihood of an assignment according to the joint model with local features will differ from the likelihood of the same assignment according to the local model only in the denominator (the partition function).


In this article, we propose several kernel functions to model parse tree properties in kernelbased machines, for example, perceptrons or support vector machines.

we define different kinds of tree kernels as general approaches to feature engineering in SRL.


We indicate with structured features the basic syntactic structures extracted from the sentence-parse tree or their canonical transformation (see Section 3.1).

we focus on the minimal spanning tree that includes the predicate along with all of its arguments.


We study different canonical mappings to capture syntactic/semantic aspects useful for SRL.

we define structured features for the different phases of the SRL process, namely, boundary detection, argument classification, conflict resolution, and proposition re-ranking.


It is worth mentioning that, as the automatic parse trees contain errors, some arguments cannot be associated with any covering node; thus we cannot extract a tree representation for them.

table 2 shows the number of arguments (column 2) for sections 2, 3, and 24 as well as the number of arguments that we could not take into account (Unrecoverable) due to the lack of parse tree nodes exactly covering their word spans.


Of course, it is possible to engineer trees including these and other standard features with a canonical mapping, but the aim here is to provide new interesting representations rather than to abide by the simple exercise of representing already designed features within tree kernel functions.

we follow the idea presented in Moschitti (2004), where tree kernels were suggested as a means to derive new features rather than generate a stand-alone feature set.